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Excess Returns

Excess Returns is dedicated to making you a better long-term investor and making complex investing topics understandable. Join Jack Forehand, Justin Carbonneau and Matt Zeigler as they sit down with some of the most interesting names in finance to discuss topics like macroeconomics, value investing, factor investing, and more. Subscribe to learn along with us.

Latest Episodes:

The $1 Trillion Supercycle Hidden in Plain Sight | Joseph Shaposhnik Mar 13, 2026

Show notes

On this episode of Excess Returns, Matt Zeigler and Bogumil Baranowski speak with Rainwater Equity ETF portfolio manager Joseph Shaposhnik about how long-term investors should think about markets in an era defined by geopolitical shocks, AI disruption, and unprecedented capital investment cycles. The conversation explores how disciplined investors can stay focused on durable businesses and long-term free cash flow rather than reacting to short-term headlines. Joseph explains how his team evaluates companies during major events, why the AI boom may create both massive disruption and opportunity, and where he believes the most attractive investment opportunities exist today.

Topics covered in this episode

• Why most macro headlines and geopolitical events rarely have lasting impacts on great businesses
• How long-term investors should analyze conflicts and market shocks without overreacting
• The defense spending supercycle and why aerospace and defense may benefit from rising geopolitical tensions
• How Joseph evaluates the AI investment cycle across semiconductors, software, and hyperscalers
• Why semiconductor companies may offer a lower-risk way to benefit from AI growth
• The risks created by massive AI infrastructure CapEx and concentration around specific AI models
• Why some software companies may face significant disruption from AI tools and LLMs
• How AI could reshape business models that rely on packaging public or commoditized data
• The potential rotation from the Magnificent Seven to the other 493 companies in the S&P 500
• Why capital intensity may change the long-term attractiveness of some technology companies
• The role of management quality and capital allocation in navigating technological disruption
• Fragile vs anti-fragile business models in an AI-driven economy
• Where AI may create unexpected winners across industrial and traditional industries
• Why long-term investors should still prioritize durable cash flow compounding businesses

Timestamps

00:00 Introduction and why most headlines have limited long-term impact on businesses
02:00 How experienced investors think about geopolitical shocks and market headlines
04:00 Defense spending tailwinds and the aerospace and defense supercycle
06:45 How investors should react when major market news breaks
11:10 How Joseph evaluates the AI boom and which companies benefit most
14:15 The case for opportunities outside the Magnificent Seven
17:15 How rising AI CapEx is changing the economics of major tech companies
21:25 Why hyperscalers face increasing concentration risk
23:00 Why semiconductor suppliers may be the best positioned AI investments
27:15 Why Joseph reduced exposure to software companies
33:00 The importance of learning organizations and adaptive management teams
37:00 AI, labor markets, and whether high-income jobs face disruption
41:00 Fragile vs anti-fragile companies in the age of AI
46:00 Where AI could create unexpected business winners
52:00 How great management teams adapt during technological disruption
57:00 How AI may accelerate entrepreneurship and innovation
59:00 Why investors should remain focused on sustainable cash flow
01:02:00 What the next generation of long-term compounders may look like

Survival First. Returns Second | Vitaliy Katsenelson on Investing Amid Extreme Uncertainty Mar 10, 2026

Show notes

In this episode of Excess Returns, Matt Zeigler and Bogumil Baranowski speak with Vitaliy Katsenelson, CEO of Investment Management Associates and author of Soul in the Game. The conversation explores how value investing is evolving in a world shaped by artificial intelligence, rapidly changing economic dynamics, and historically high market valuations. Vitaliy discusses why humility and diversification are increasingly important for investors today, how to balance quality and valuation when selecting stocks, and what he has learned about selling decisions, portfolio construction, and long-term investing discipline. The discussion also moves beyond markets into deeper ideas about passion, creativity, and why investing, like art, is ultimately a creative pursuit driven by curiosity and lifelong learning.

Topics covered in this episode

Why high stock market valuations may create a headwind for future returns
The math behind long-term stock market returns and the role of earnings growth versus valuation changes
Whether the dominance of mega-cap technology companies represents a structural shift in markets
Why AI investment could lead to both massive innovation and large amounts of wasted capital
The importance of humility in investing during periods of rapid technological and economic change
Why Vitaliy increased the number of stocks in his portfolio due to greater uncertainty
How investors can think about what will not change in a rapidly evolving world
The evolution from statistical value investing to focusing on business quality and management
Why cheap stocks are often expensive and how narrative bias can trap value investors
The importance of evaluating management integrity and avoiding companies with questionable leadership
How Vitaliy thinks about selling decisions and recognizing when an investment thesis is broken
Why many investors make their biggest mistakes by selling winners too early
The concept of being a value buyer but a growth holder when fundamentals improve
Why updating valuation models as businesses improve is critical to capturing long-term upside
Lessons learned from great investors and the importance of surrounding yourself with thoughtful peers
The idea of building a personal operating system for investing and life
Passion, patience, and process as the three pillars of long-term investment success
Why investing is fundamentally a creative pursuit similar to art and music
The deeper motivations behind investing and why for many great investors it is not ultimately about money

Timestamps

0:00 Vitaliy on humility and why the range of outcomes in investing is expanding
2:00 The math behind long-term stock market returns
4:00 Why high valuations can become a headwind for future returns
6:00 Big tech growth and whether large companies now have structural advantages
8:00 AI investment and the risk of massive capital misallocation
10:30 Learning AI and why investors must adapt to rapid technological change
14:00 Why humility leads to diversification and larger portfolios
20:00 The evolution from cheap stocks to quality investing
25:30 Selling discipline and recognizing when a thesis is broken
34:30 Letting winners run and avoiding the mistake of selling too early
42:00 Learning from other great investors and building your own framework
44:30 Passion, patience, and process in investing
52:00 Why great investors are motivated by more than money
1:01:40 The connection between investing, creativity, and classical music

What War Charts and AI Bubbles Miss | The Weekly Market Insight – March 8, 2026 Mar 09, 2026

Show notes

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In this new weekly Excess Returns recap, Jack Forehand and Matt Zeigler highlight the most important investing insights from recent conversations across the Excess Returns podcast network. Drawing on discussions with Andy Constan, Rob Arnott, Kai Wu, Ben Hunt, Rupert Mitchell, Meb Faber and others, the episode connects ideas across macro, markets, AI, credit cycles and valuation. The conversation focuses on timeless investing principles investors can apply today, including how to evaluate expert opinions, how AI may reshape markets and jobs, what defines a true market bubble, why international stocks may be benefiting from global fiscal spending, and why the best opportunities in markets often come after long periods of underperformance.

Topics covered in this episode

How to evaluate expert opinions during major market events and filter signal from noise
Andy Constan’s framework for judging credibility based on experience and confidence
Why charts showing markets rising after wars are often misleading data mining
The difference between believing in AI technology and believing AI stocks are good investments
How AI could both replace and augment human work through the task based structure of jobs
Rob Arnott’s definition of a market bubble using implausible growth assumptions
Why many technology leaders ultimately fail to justify the expectations priced into their stocks
The difference between software companies whose moat is code and those with durable intangible advantages
How brand, switching costs, distribution and network effects protect enterprise software companies
Why AI may be one of the most disruptive technologies in history and what that means for markets
Meb Faber on the myth that the easy money has already been made in international and value stocks
The behavioral challenge of holding unpopular strategies through long periods of underperformance
Rob Arnott on why small cap value could outperform large cap growth over the next decade
Ben Hunt on the point in every credit cycle when lenders say no more
How rising costs of capital can trigger boom bust credit cycles
Rupert Mitchell on why global equity markets often follow government fiscal spending
The growing role of international fiscal policy and capital flows in global market leadership

Timestamps

00:00 Introduction and the idea behind the weekly Excess Returns recap show
03:00 Andy Constan on how to evaluate experts and filter market commentary
11:40 Why charts showing markets rising after wars can be misleading
17:00 Kai Wu on AI technology versus AI investments and the future of work
25:37 Rob Arnott on how to define a market bubble using valuation assumptions
29:35 Kai Wu on software moats, intangible assets and enterprise software durability
35:31 Rob Arnott on how disruptive AI could be for the global economy
39:54 Meb Faber on why the easy money has never been made in markets
43:57 Rob Arnott on small cap value versus large cap growth opportunities
48:39 Ben Hunt on credit cycles and the moment lenders pull back
55:56 Rupert Mitchell on fiscal spending and global equity market performance

1% Growth. Zero Jobs | Jim Paulsen on the Recession Hiding in Plain Sight Mar 07, 2026

Show notes

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In this episode of the Jim Paulsen Show, Jim joins Jack Forehand and Justin Carbonneau to break down the macro forces shaping today’s markets and economy. Jim explains why the economy may be far weaker than headline GDP numbers suggest, how technology and AI investment are masking weakness in the broader economy, and why leadership in the stock market may be shifting. The conversation also explores the market implications of geopolitical conflict, the relationship between policy and market leadership, and how investors should think about AI’s long-term economic impact.

Topics covered in this episode

How geopolitical events like the Iran conflict affect markets, volatility, oil prices, and investor sentiment
Why market reactions to geopolitical shocks often fade once the situation is “vetted” by investors
The relationship between oil prices, the US dollar, and global financial markets
Why Paulsen remains constructive on international stocks and emerging markets despite recent volatility
Why energy and food now represent a much smaller share of consumer spending than in past inflation cycles
The argument that inflation fears may be overstated given structural disinflationary forces in the economy
How AI and technological innovation can destroy some jobs while simultaneously creating new economic demand
Why technological progress often lowers costs and expands markets rather than simply eliminating work
The concept that the “new economy” driven by technology investment is now large enough to influence overall GDP growth
Paulsen’s analysis showing that roughly 11 percent of the economy tied to new-era investment is growing rapidly while the remaining 89 percent is barely growing
Why the broader economy may resemble a recession even while headline GDP remains positive
How the dominance of large technology companies in indexes like the S&P 500 may be masking weakness in the broader market
The historical “toggle” between technology leadership and broader market leadership in equity markets
Why policy conditions like the yield curve and monetary easing often drive leadership shifts toward value, small caps, and cyclical stocks
Whether the Federal Reserve could begin easing policy without a traditional recession
Why policy support may eventually broaden the bull market beyond technology stocks

Timestamps

0:00 Jim Paulsen on geopolitical volatility, oil prices, and market reactions
2:50 How investors should think about the Iran conflict and market implications
10:50 The relationship between oil prices, the US dollar, and safe-haven flows
12:20 Why Paulsen likes international and emerging market stocks
14:30 Why higher oil prices may not lead to sustained inflation
18:40 AI disruption and the economic debate around jobs and productivity
23:00 How innovation historically creates new demand and economic growth
29:40 Technology is the tail wagging the economic dog
33:30 Why the “new economy” is growing far faster than the rest of the economy
37:00 Evidence that most of the economy may already resemble a recession
41:00 Profit growth disparity between technology and the rest of the economy
45:40 Why the stock market can mask weakness in the broader economy
46:30 The historical leadership toggle between tech and the broader market
49:00 Valuation differences between technology and other sectors
50:30 How policy conditions influence market leadership
55:00 Signs that leadership may already be shifting beyond tech
57:00 Could the Fed ease without a traditional recession
59:00 What a policy shift could mean for the next phase of the bull market

The Widest Valuation Gap in History | Rob Arnott on What Investors Are Missing About AI Mar 05, 2026

Show notes

Rob Arnott returns to Excess Returns to discuss the biggest questions facing investors today, including the impact of geopolitical conflict, the valuation gap between U.S. and international markets, the long-term investment implications of artificial intelligence, and why extreme spreads between growth and value may present major opportunities. Arnott, founder of Research Affiliates and pioneer of fundamental indexing, explains why AI itself is not necessarily a bubble but many AI stocks may be priced for implausible growth. He also discusses why small cap and value stocks may offer some of the most compelling long-term opportunities in decades, how market narratives drive valuations, and why diversification beyond the U.S. could be critical for investors. Throughout the conversation, Arnott draws on decades of market history to explain how bubbles form, why profit margins tend to mean revert, and how investors should think about positioning portfolios for the next market cycle.

Topics covered in this episode:

• Why Rob Arnott believes AI is real but many AI stocks may be in a bubble
• How market narratives can push valuations far beyond fundamentals
• Why U.S. stocks trade at roughly twice the valuation multiples of international markets
• The widening valuation gap between growth and value stocks
• Why small cap stocks may be one of the most attractive opportunities today
• The massive capital spending required to build the AI ecosystem
• How technological revolutions historically destroy jobs but create new opportunities
• Why investors should learn to use AI tools to remain competitive
• The definition of a market bubble based on implausible growth expectations
• Lessons from the dot-com bubble and the history of dominant technology companies
• Why profit margins tend to mean revert over time
• The long-term outlook for international stocks and diversification
• How fundamental indexing works and why it can create rebalancing alpha
• The concept of the “Trifecta” approach combining value, core indexing, and growth
• The risks of conglomerate premiums and the diversification discount
• Why the largest companies in the market rarely remain dominant over long periods
• How investors should think about balancing growth exposure with cheaper opportunities

Timestamps:

00:00 AI vs AI Stocks: Why Arnott Sees a Bubble
00:01 Introduction to Rob Arnott and Research Affiliates
02:13 The Iran Conflict and How War Impacts Markets
06:41 U.S. Valuations vs International Opportunities
08:50 The Extreme Spread Between Growth and Value
10:00 The Small Cap Opportunity and Index Effects
13:08 The Citrini AI Paper and Long-Term Technology Shifts
14:09 How Technological Revolutions Destroy and Create Jobs
16:00 How AI Is Already Changing Investment Research
20:00 Why AI Tools Are Still Losing Money
23:40 How Investors Should Think About AI Exposure
25:21 Arnott’s Definition of a Market Bubble
27:41 Lessons from the Dot-Com Bubble
28:34 Profit Margins and Mean Reversion
30:34 Technology Moats and Competitive Disruption
32:12 Will Mean Reversion Still Work in Markets?
36:02 The Case for International Stocks
41:39 The Trifecta: A New Framework for Indexing
51:15 Why Expensive Slow-Growth Companies Underperform
56:25 Conglomerate Premiums and Mega Cap Tech
57:00 The Long-Term Case for Value and Small Caps
01:00:00 Why Market Leaders Rarely Stay on Top

100% Out of US Stocks | Andy Constan on AI, War Risk and the Shift Abroad Mar 03, 2026

Show notes

In this episode of Excess Returns, we welcome back Andy Constan of Damped Spring Advisors for a wide-ranging discussion on geopolitical risk, AI and productivity, capital flows, credit markets, fiscal policy, and the shift from US to international equities. Andy walks through the framework he uses to evaluate uncertainty, from wars and geopolitical shocks to the long-term implications of artificial intelligence, and explains why capital markets and funding conditions may matter more than bold narratives. We also explore growth, inflation, Fed policy, and the structural case for global diversification in today’s macro environment.

Main topics covered

A practical framework for analyzing geopolitical shocks, including red flags, green flags, and how to evaluate information quality during times of uncertainty
How markets are pricing the current conflict with Iran across oil, equities, bonds, gold, and volatility
Why historical market performance after wars may offer limited predictive value due to small sample sizes
How to think about AI from a macro perspective, including GDP growth versus GDP share and who ultimately captures the gains
The capital markets implications of massive AI-related capex and whether equity and credit markets can fund current spending plans
Growth, inflation, and the Fed: how fiscal stimulus, wealth effects, QT, and labor market trends are shaping the current macro backdrop
Why Andy has shifted away from US assets toward international markets, including the role of bond yields and global risk parity
A critical look at the Trump accounts proposal and the broader issue of fiscal deficits and capital allocation
The key risks Andy is watching over the next three to six months, especially around credit markets and funding conditions

Timestamps

00:00 Introduction and overview of discussion topics
01:01 Framework for evaluating geopolitical shocks and information quality
11:46 Market reaction to the Iran conflict and asset pricing implications
23:00 Why historical war data may not be reliable for market forecasting
27:03 How to analyze AI’s impact on productivity and economic growth
37:00 AI capex, credit markets, and funding risks
42:24 Growth, inflation, and Fed policy in the current cycle
49:20 The case for international equities over US markets
56:20 Trump accounts, fiscal policy, and capital allocation
01:02:23 What Andy is watching most closely in the months ahead

Is AI Replacing Workers Faster Than We Think? | We Break Down the Viral AI Doom Loop Article Mar 01, 2026

Show notes

In this episode, Jack Forehand and Kai Wu break down the viral “AI doom loop” article that sparked debate across Wall Street, Silicon Valley, and even the Federal Reserve. They walk through the core thesis that artificial intelligence could trigger a non-cyclical economic disruption, separating signal from noise and exploring what it could mean for software stocks, labor markets, productivity, wealth inequality, and long-term investing. Rather than reacting emotionally, they analyze the mechanics step by step, asking whether AI is more likely to replace workers or amplify them, how fast adoption can realistically happen, and what investors should be watching right now.

Main topics covered:

The core thesis behind the AI doom loop scenario and why it went viral
Is AI a substitute for human labor or a productivity multiplier
People times productivity as a framework for understanding economic growth
Why we are not yet seeing major AI disruption in labor or productivity data
Software stocks, margin compression, and the risk to SaaS business models
The Jevons Paradox and whether lower costs could expand demand instead of destroy it
Why incumbents with strong intangible moats may survive AI disruption
The difference between technological capability and real world adoption speed
Compute, energy, and token costs as natural limits on AI expansion
The feedback loop argument and whether AI could cause a demand shock
Creative destruction and the difficulty of forecasting new job creation
AI, high income knowledge workers, and the risk to consumer spending
Wealth inequality, capital versus labor, and policy responses like UBI
Why investors can be bullish on AI technology but cautious on markets
How to think about short term disruption versus long term abundance

Timestamps:

00:00 Introduction and the AI doom loop thesis
02:15 Why the article triggered a market reaction
06:00 People times productivity and economic growth
09:00 AI and disruption in software stocks
15:00 Jevons Paradox and expanding total demand
19:00 AI agents, frictionless commerce, and price competition
26:00 Adoption speed versus technology speed
28:00 Compute constraints and natural governors on AI growth
31:00 The non cyclical disruption feedback loop
33:00 Creative destruction and new job formation
38:00 General purpose technology and broad economic exposure
44:00 Replacement versus augmentation of workers
48:00 Token costs, enterprise AI spending, and labor tradeoffs
51:00 High income job risk and inequality concerns

The AI Panic Trade | What the Viral Doomsday AI Article Means for Markets Feb 28, 2026

Show notes

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In this episode of Last Call, Jack Forehand and Matt Zeigler look past the headlines to unpack what really moved markets this month. From the viral AI end of times scenario that sparked responses from Citadel, Fed Governor Waller, and Jeremy Siegel, to the growing stress in private credit and the rotation out of US mega cap stocks, this is a different kind of market wrap. Instead of recapping what the S and P 500 did, we explore what investors are actually doing with their money, how narratives shape positioning, and what the data says about whether this time is different.

Featuring Brent Kochuba of SpotGamma, Ben Hunt of Epsilon Theory, Rupert Mitchell of Blind Squirrel Macro, and Meb Faber of The Idea Farm, this episode dives into AI, software stocks, options flows, credit cycles, global equity markets, gold, and the power of base rates in investing.

Main topics covered:

The viral AI bear case scenario and why a fictional narrative moved real markets
How investors should think in probabilities, bull cases, base cases, and bear cases
What options pricing and put call ratios reveal about real fear versus social media fear
The state of software stocks and whether extreme bearishness may have marked a short term bottom
Private credit stress, rising default risks, and why every credit cycle ends when lenders say no more
An on the ground anecdote from San Francisco illustrating how refinancing risk is playing out in real time
The rotation from US mega caps into international stocks and why fiscal spending matters for equity markets
Gold and gold miners as potential beneficiaries of global liquidity and currency shifts
Why base rates matter when evaluating explosive AI revenue forecasts
Historical lessons from the Nifty Fifty, Japan’s bubble, the dot com era, and other periods when investors believed this time is different
Portfolio construction tools including diversification, rebalancing, and trend following in bubble environments

Timestamps:

00:00 Introduction and the AI end of times narrative
02:16 Why investors are responding to fiction and what we can learn from it
08:00 Brent Kochuba on options flows and software stock positioning
13:00 Has extreme bearishness in software marked a bottom
19:55 Ben Hunt on private credit and the boom bust cycle
27:00 A San Francisco refinancing story and when lenders say no
33:08 Rupert Mitchell on global markets, fiscal spending, and gold
44:22 Meb Faber on base rates, bubbles, and this time is different
01:00:16 How to track AI’s real world impact in corporate data

If you enjoy deep dives into investing, AI, market structure, credit cycles, global equities, and evidence based portfolio construction, be sure to subscribe to Excess Returns for more conversations like this.

Most Portfolios Are Built Backwards | Cullen Roche on Building Your Perfect Portfolio Feb 27, 2026

Show notes

In this episode of Excess Returns, we sit down with Cullen Roche to discuss his new book Your Perfect Portfolio and the deeper principles behind building a portfolio that actually fits your life. Rather than starting with asset allocation models or return forecasts, Cullen reframes investing around risk, time horizons, and lifetime consumption. We explore how to think about stocks, bonds, factor investing, international diversification, private assets, inflation hedges, and more through the lens of financial planning and asset liability matching. This is a practical, wide ranging conversation about portfolio construction, behavioral risk, and how investors can align their investments with real world goals.

Main topics covered:

Why you are a saver, not an investor, and why that distinction matters
Defining risk as uncertainty of lifetime consumption
The temporal conundrum and matching investments to time horizons
Human capital as your most important asset and how it impacts portfolio risk
The pros and cons of a 100 percent stock allocation
Rethinking the 60 40 portfolio after inflation and rising rates
International diversification and valuation differences between US and global markets
Factor investing as a time horizon tool rather than an alpha strategy
The forward cap portfolio and skating to where the market cap puck is going
Inflation protection strategies including stocks, TIPS, gold, and the permanent portfolio
Risk parity and the tradeoff between diversification and return
Countercyclical rebalancing and managing behavioral risk
Private equity, venture capital, and the illiquidity premium
Defined duration investing and asset liability matching for individual investors
The real impact of inflation, taxes, and fees on long term returns

Timestamps:

00:00 Risk as lifetime consumption and asset liability matching
01:03 Introduction to Your Perfect Portfolio
05:25 You are a saver, not an investor
08:24 Defining risk and uncertainty of lifetime consumption
10:15 The temporal conundrum and time horizons
12:38 Using past performance and forecasting responsibly
15:00 Human capital and portfolio construction
17:12 The case for a 100 percent stock allocation
19:50 Rethinking the 60 40 portfolio
24:00 Adding international diversification
29:43 Factor investing across time horizons
35:00 The forward cap portfolio concept
38:27 Inflation hedges and the permanent portfolio
42:27 Risk parity explained
44:49 Countercyclical rebalancing
47:17 Private assets and illiquidity
51:25 Defined duration strategy and Discipline Funds ETFs
56:00 Real returns after inflation, taxes, and fees

If you are interested in portfolio construction, asset allocation, financial planning, factor investing, inflation protection, or building a long term investment strategy that matches your goals, this conversation offers a thoughtful framework for thinking differently about risk and returns.

The Edge Has Shifted | Matt Reustle on How the Best Investors Use AI Feb 25, 2026

Show notes

In this episode of Excess Returns, we sit down with Matt Russell of Business Breakdowns to explore how AI is actually being used in investing today. We go beyond the hype and break down practical use cases for AI in portfolio management, stock research, due diligence, monitoring, and idea generation. From deep research models and agentic AI to prompt engineering and workflow design, this conversation walks through how professional investors can use AI tools to increase productivity, improve decision-making, and reduce blind spots without losing their edge. If you are an asset manager, analyst, allocator, or DIY investor wondering how AI will impact investing and stock picking, this episode offers a clear, practical roadmap.

Main topics covered:

The evolution from early large language models to deep research and agentic AI for investors
LLMs vs agent-based AI and why the distinction matters for investment research
How AI fits into an investor’s workflow, from due diligence to portfolio monitoring
Using AI to monitor KPIs, earnings calls, and cross-industry signals in real time
How AI can help kill bad ideas faster and surface deal breakers early
Prompt engineering for investors, including mindset framing, audience targeting, and output design
Building mental models into AI systems to reflect your investment philosophy
AI tech stacks for investors, including writing tools, deep research models, and browser-based AI
Iteration, experimentation, and standardized testing of prompts across model upgrades
The impact of AI on alpha generation, active management, and generalist vs specialist investors
Organizational adoption strategies for investment firms considering AI
Customization, agentic workflows, and what AI in investing could look like five years from now

Timestamps:

00:00 How AI tools increase investor productivity
01:16 Why early ChatGPT was a head fake for investors
03:07 The inflection point with deep research and agentic AI
05:00 LLMs vs agents explained in plain English
07:01 Where AI fits inside an investment workflow
09:28 Replacing manual earnings transcript work
11:40 Real-time monitoring and AI alerts
19:24 Using AI to kill bad investment ideas faster
22:01 Trust but verify, hallucinations and safeguards
25:29 Matt’s AI tech stack for investing
30:00 Prompt engineering breakthroughs
33:00 Standardized experimentation across new AI models
36:07 Building idea generation prompts step by step
40:15 Using AI as an editor and critical reviewer
43:50 Does AI compress investor skill differences
46:10 How funds should adopt AI internally
50:40 Fear of falling behind in asset management
53:05 Generalists vs specialists in an AI world
55:18 AI and the pursuit of alpha
57:00 Customization, agents and the future of investing
01:01:10 Coding agents and building tools with AI

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